| DC 欄位 |
值 |
語言 |
| DC.contributor | 電機工程學系 | zh_TW |
| DC.creator | 曾昱翔 | zh_TW |
| DC.creator | Yu-Xiang Zeng | en_US |
| dc.date.accessioned | 2022-8-25T07:39:07Z | |
| dc.date.available | 2022-8-25T07:39:07Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=109521127 | |
| dc.contributor.department | 電機工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 真實生活中人們遇到醫療問題，經常藉由不同的管道，尋求醫生的建議與解答，而自動問答系統提供一個即時回覆答案的解決方案。本研究的主要目標為建立中文醫療問答系統，將問題輸入問答系統，從醫療問答資料集中，匹配找出最佳的答案返回給使用者。近年來，不同於傳統的詞彙匹配，深度學習的興起帶動了語義匹配的方式，深度語言模型能有效學習文本的語義訊息，並藉此找出相近的文本。許多研究均顯示出語義匹配的方法較傳統的方法得到更好的效果，因此，我們提出句嵌入向量重排序器 (Sentence Embedding Reranker, SER) 模型。
中文問答資料來自於醫聯網 (https://med-net.com/)，資料集共有 26,816 筆醫療問答，我們使用 Pooling method 建立系統測試集，從 26,816 筆問題中取 120 筆問題作為測試問題，每個問題分別經過兩個不同的檢索系統 (BM25 以及 Sentence-BERT)，返回100 筆答案，並人工標註其答案的正確性，最後取兩系統的聯集作為系統測試集。藉由實驗結果得知，我們提出的 SER 重排序器模型，在 MAP、NDCG 效能指標達到最好的分數，有效增進中文問答系統的檢索效能。 | zh_TW |
| dc.description.abstract | In the digital era, users usually search and browse web content to obtain healthcare related information before making a doctor’s appointment for diagnosis and treatment. The automatic question-answering system can provide a solution to address this need in real-time. Our main
research objective is to design and implement a Chinese medical question answering system.
In such a medical QA system, users issue a question as a query and then obtain relevant doctors’ answers in the ranked list. Different from traditional lexical matching methods, the deep learning-based semantic matching model can effectively learn the semantic features to retrieve
similar texts. Therefore, we propose a Sentence Embedding Reranker (SER) model to enhance the question-answering performance.
The Pooling method was used to combine the top 100 results returned by BM25 and Sentence-BERT retrieve systems for answer relevance annotation. Based on experimental results from these manual-annotated question-answer pairs, our proposed SER re-ranking model achieved the best results in MAP and NDCG, which can enhance the performance of the Chinese medical question-answering system. | en_US |
| DC.subject | 醫療問答系統 | zh_TW |
| DC.subject | 資訊檢索 | zh_TW |
| DC.subject | 預訓練語言模型 | zh_TW |
| DC.subject | 語義匹配 | zh_TW |
| DC.subject | Medical question-answering | en_US |
| DC.subject | information retrieval | en_US |
| DC.subject | pre-trained language models | en_US |
| DC.subject | semantic search | en_US |
| DC.title | 運用句嵌入向量重排序器 增進中文醫療問答系統效能 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Enhancing Chinese Medical Question-Answering Performance with Sentence Embedding Reranker | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |